{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "import gym\n",
    "import numpy as np\n",
    "from matplotlib import pyplot as plt\n",
    "from collections import defaultdict\n",
    "\n",
    "import gym\n",
    "env = gym.make('Blackjack-v1') \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "导入相关的包，以及导入BlackJack环境"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def greedy(q,epsilon,nA):\n",
    "    def make_policy(observation):\n",
    "        A=np.ones(nA,dtype=float)*epsilon/nA\n",
    "        bestac=np.argmax(q[observation])\n",
    "        A[bestac]+=1-epsilon\n",
    "        return A\n",
    "    return make_policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "贪心策略,因为mc是逐幕计算的，该策略用于计算MC中的一幕数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "def MC_greedy(env,episodes,discount=1.0,dec=1e-6):\n",
    "    sa_return_sum = defaultdict(float)\n",
    "    sa_count = defaultdict(float)\n",
    "    q = defaultdict(lambda: np.zeros(env.action_space.n))\n",
    "    qlist=[]\n",
    "    for i in range(1,episodes):\n",
    "        policy = greedy(q, 0.1-dec*i, env.action_space.n)\n",
    "        episode=[]\n",
    "        state=env.reset()\n",
    "        for t in range(100):\n",
    "            probs = policy(state)\n",
    "            action = np.random.choice(np.arange(len(probs)), p=probs)\n",
    "            next_state, reward, done,_= env.step(action)\n",
    "            episode.append((state, action, reward))\n",
    "            if done:\n",
    "                break\n",
    "            state = next_state\n",
    "        sa_episode=set([(tuple(x[0]),x[1]) for x in episode])\n",
    "        for state,action in sa_episode:\n",
    "            G=0\n",
    "            delta_Q=0\n",
    "            sa=(state,action)\n",
    "            Q_old=q[state][action]\n",
    "            for j,x in enumerate(episode):\n",
    "                if state==x[0] and action==x[1]:\n",
    "                    first=j\n",
    "                    break\n",
    "            for j,x in enumerate(episode[first:]):\n",
    "                G+=x[2]*discount**j\n",
    "            sa_return_sum[sa]+=G\n",
    "            sa_count[sa]+=1\n",
    "            q[state][action]=sa_return_sum[sa]/sa_count[sa]\n",
    "            delta_Q+=(q[state][action]-Q_old)**2\n",
    "            \n",
    "\n",
    "            \n",
    "        print('loss={}'.format(delta_Q))\n",
    "        qlist.append(delta_Q)\n",
    "    x=range(4999)\n",
    "    plt.xlabel('')\n",
    "    plt.ylabel('loss')\n",
    "    plt.title(r'loss')\n",
    "    plt.plot(x, qlist)\n",
    "    plt.show()\n",
    "\n",
    "    return policy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "蒙特卡洛方法得到策略"
   ]
  },
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   "cell_type": "code",
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   "metadata": {},
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    {
     "name": "stderr",
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     "text": [
      "/home/wzy/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/wzy/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 27425 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/wzy/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:238: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0.0, flags=flags)\n",
      "/home/wzy/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 27425 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/wzy/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 25968 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n",
      "/home/wzy/anaconda3/lib/python3.8/site-packages/matplotlib/backends/backend_agg.py:201: RuntimeWarning: Glyph 22270 missing from current font.\n",
      "  font.set_text(s, 0, flags=flags)\n"
     ]
    },
    {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "胜率=0.3012\n"
     ]
    }
   ],
   "source": [
    "policy=MC_greedy(env,episodes=5000,discount=1,dec=2*1e-6)\n",
    "win=0\n",
    "total=5000\n",
    "for i in range(total):\n",
    "    observation=env.reset()\n",
    "    for t in range(30):\n",
    "        probs = policy(observation)\n",
    "        action = np.random.choice(np.arange(len(probs)), p=probs)\n",
    "        next_state, reward, done,_= env.step(action)\n",
    "        if done:\n",
    "            if reward==1:\n",
    "                win+=1\n",
    "            break\n",
    "        state = next_state\n",
    "acc=win/total\n",
    "print('胜率={}'.format(acc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "interpreter": {
   "hash": "7648c2b9d25760d0d65f53f9b9a34de48caa24d8265d64b0ff81e2f2641d528d"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
